1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Exhibit
oci generative-ai inference chat \
--endpoint https://inference.generativeai.us-chicago-1.oci.oraclecloud.com \
--model-id "cohere.command" \
--compartment-id "ocid1.compartment.oc1..example" \
--messages '[{"role":"user","content":"Explain quantum computing."}]' \
--temperature 0.7 \
--top-p 0.9 \
--max-tokens 500Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?
⚠ Common exam trap
Oracle often tests the distinction between parameters that control output length (max-tokens) versus those that control output diversity (temperature, top-p), leading candidates to confuse the effect of randomness with truncation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Max-tokens is too low
The 'max-tokens' parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When set too low, the model is forced to stop generating output before completing its thought, resulting in a truncated or cut-off sentence. This is the most direct cause of an output that ends abruptly mid-sentence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Max-tokens is too low
Why this is correct
If the output exceeds max-tokens, it gets truncated, causing the cut-off.
- ✗
Temperature is too high
Why it's wrong here
Temperature affects randomness, not output length.
- ✗
Model ID incorrect
Why it's wrong here
An incorrect model ID would likely result in an error, not a short response.
- ✗
Top-p is too high
Why it's wrong here
Top-p influences sampling diversity, not length.
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